Abstract: Dataset distillation compresses large datasets into smaller synthetic coresets that retain performance with the aim of reducing storage and computational burdens of processing an original, entire dataset. The present disclosure provides an improved algorithm that uses a non-deterministic feature approximation of neural network Gaussian process (NNGP) kernels, or other trained kernels, that reduces a kernel matrix computation to O(|S|). When combined with a modified Platt scaling loss, the disclosed algorithm can provide at least a 100-fold speedup over a Kernel-Inducing Points (KIP) algorithm and can run on a single graphics processing unit. The disclosed Random Feature Approximation Distillation (RFAD) algorithm can perform competitively with other dataset condensation algorithms in accuracy over a range of large-scale datasets, both in kernel regression and finite-width network training. The disclosed techniques can be effective on tasks such as model interpretability and data privacy preservation.